MKT5612 Chap.5 Sampling Designs and Survey Error
Sampling Designs and Survey Error
A sampling plan links the target population to the units that can actually be selected. The target population, sampling unit and frame must be distinguished before choosing a design.
Probability samples give units known, nonzero selection chances and support design-based inference; nonprobability approaches may be faster or useful for specialised access but do not justify a margin of sampling error merely because the sample is large.
Simple random, systematic, stratified and cluster designs solve different operational problems.
Systematic sampling requires an interval and a random start, while stratification protects representation of defined subgroups. Sampling error is only one part of total survey error. Coverage, nonresponse, measurement and processing error can dominate a precisely calculated sample, so design quality depends on recruitment, follow-up, instrument quality and data handling as well as the selection formula.
What this chapter covers
- 01
Target populations, units and frames
- 02
Probability and nonprobability designs
- 03
Simple random and systematic selection
- 04
Stratified and cluster designs
- 05
Coverage, nonresponse and measurement error
- 06
Total survey error and inference boundaries
Worked example · free
Design a systematic alumni sample
- 3Calculate and interpret the interval.
- 3List the first four selected positions.
- 4Identify a coverage or ordering risk and a control.
Key terms
- Target Population
- The complete set of elements about which the study intends to draw conclusions.
- Sampling Frame
- The operational list or mechanism used to identify units available for selection.
- Probability Sample
- A design in which selection is governed by chance and units have known, nonzero inclusion probabilities.
- Systematic Sampling
- Probability selection using a random start followed by units separated by a calculated interval on the frame.
- Coverage Error
- Error arising when the frame excludes target units, includes ineligible units or duplicates entries.
- Nonresponse Error
- Distortion that occurs when response is related to study variables and respondents differ from nonrespondents.
- Total Survey Error
- A framework considering sampling, coverage, nonresponse, measurement and processing errors together.
Sampling Designs and Survey Error FAQ
Does a large convenience sample become representative?
No. Size can reduce random sampling variability only within a design that supports the calculation. It cannot repair unknown selection mechanisms, systematic undercoverage or response patterns that differ with the variables being studied.
Why does systematic sampling need a random start?
The random start gives units in the first interval a chance to determine the selection sequence. Without it, a fixed starting position can embed investigator convenience or a frame-order pattern into every subsequent selection.
When is stratification useful?
Stratification is useful when defined subgroups must be represented or differ meaningfully on the study outcome. Sampling independently within strata can improve precision and guarantees planned subgroup presence, though weighting may be required after disproportionate allocation.
Can weighting repair every imbalance in a survey?
No. Weighting can adjust represented groups when their population shares and inclusion or response patterns are known, but it cannot recover a group absent from the frame or correct inaccurate answers. Large weights can also increase sampling variability.
Exam move
Start every sampling case by writing four nouns: population, unit, frame and respondent. If any pair is silently treated as identical, look for coverage or eligibility error. Then choose a design by the operational structure, not by a memorised hierarchy: systematic selection suits a usable list, stratification protects subgroups and clusters reduce field dispersion.
Show numerical selection steps cleanly, including the random start. Finish with a total-error paragraph that names at least one nonsampling threat and a plausible control, because a correct interval or sample-size number never proves the resulting survey is unbiased. Practise systematic draws on short numbered frames so the interval, random start and periodicity check become automatic.
For stratified examples, write the inclusion process inside every stratum and decide whether weights are needed for a whole-population estimate. For cluster examples, explain how within-cluster similarity affects precision instead of treating lower travel cost as a free benefit. Build an error ledger with five rows: sampling, coverage, nonresponse, measurement and processing.
Add one mechanism and one targeted control to each row. When revising a case answer, delete any proposed remedy that acts on a different error from the one diagnosed. This keeps sample-size arithmetic from dominating the broader survey design.
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